#Class 05: Data Visualization
#Today we are going to use ggplot2
#Load package
library(ggplot2)
#Use inbuilt "cars" dataset first, confirm content with head function
head(cars)
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
#All ggplots have at least data + aes + geom layers
ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point() +
geom_smooth(method="lm") +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title="Stopping Distance of Cars", x="Speed (MPH)", y="Stopping Distance (ft)")
## `geom_smooth()` using formula 'y ~ x'

#ggplot is not the only graphics system, "base" R has one built in too
plot(cars)

#Pull in RNA-seq dataset
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
#Interrogate features of genes data frame
#Number of genes
nrow(genes)
## [1] 5196
#Names and amounts of columns
colnames(genes)
## [1] "Gene" "Condition1" "Condition2" "State"
ncol(genes)
## [1] 4
#Pull numbers of downregulated, unchanging, and upregulated genes
table(genes$State)
##
## down unchanging up
## 72 4997 127
#Calculate % of each state
round((table(genes$State)/nrow(genes))*100, 2)
##
## down unchanging up
## 1.39 96.17 2.44
#Generate RNA-seq plot
RNA_plot <- ggplot(genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
#Display plot
RNA_plot

#Adjust colors and add labels
RNA_plot <- RNA_plot + scale_color_manual(values=c("blue", "gray", "red"))
RNA_plot <- RNA_plot + theme(plot.title = element_text(hjust = 0.5)) +
labs(title="Gene Expression Changes Upon Drug Treatment",
x="Control (no drug)", y="Drug Treatment")
#Display new plot
RNA_plot

#Exploring gapminder dataset
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
#Plot year vs life expectancy
ggplot(gapminder) +
aes(x=year, y=lifeExp, col=continent) +
geom_jitter(width=0.3, alpha=0.4) +
geom_violin(aes(group=year), alpha=0.2, draw_quantiles=0.5)

#Load plotly
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplotly()